With the increasing demand for security and safety, video-based surveillance systems are being increasingly implemented in urban locations. Vast amounts of video footage are collected and analyzed for traffic violations, accidents, crime, terrorism, vandalism, and other suspicious activities. Since manual analysis of such large volumes of data is prohibitively costly, there is a desire to develop effective algorithms that can aid in the automatic or semi-automatic interpretation and analysis of video data for surveillance and law enforcement. An active area of research within this domain is video anomaly detection, which refers to the problem of finding patterns in data that do not conform to expected behavior, and that may warrant special attention or action.
Video-based AD (Anomaly Detection) has received much recent attention. One class of techniques relies upon object tracking to detect nominal object trajectories and deviations thereof. This approach is appealing for traffic-related anomalies since there are many state-of-the-art tracking techniques that can be leveraged. A common approach involves derivation of nominal vehicle paths and identification of deviations thereof in, for example, live traffic video data. During a test or evaluation phase, a vehicle can be tracked and its path compared against the nominal classes. A statistically significant deviation from all classes indicates an anomalous path.
Primary challenges in AD include, but are not limited to: i) successful detection of abnormal patterns in realistic scenarios involving multiple object trajectories in the presence of occlusions, clutter, and other background noise; ii) development of algorithms that are computationally simple enough to detect anomalies in quasi-real-time; and iii) the lack of sufficient and standardized data sets, particularly those capturing anomalous events which are rare by definition.